1. Field of the Invention
The present invention is directed to computer systems. More particularly, it is directed to image processing and computer vision.
2. Description of the Related Art
Image processing often involves the detection of various linear and circular image features in image data. For example, linear and circular image feature detections may be used in many image processing and computer vision applications. Linear features may include features such as straight lines, edges and corners. Circular features may include features such as circular arcs, ellipses, among others.
Variations of Hough transform have been widely used in image processing applications. The Hough transform is a robust method for detecting such both linear and circular image features, even in the presence of significant noise in the image. The basic idea of the Hough transform is to translate the original feature detection problem into an equivalent problem of peak detection in the parametric space for the lines and circular arcs. Because of this parametric analytic formulation, many existing Hough transform methods assume zero stroke width (or 1-pixel width in a discrete realm) for the line and circular image features. However, this assumption does not typically hold for the natural scenes.
Another issue is that most linear and circular feature detectors use edge operators, such as the Sobel operator, which usually do not generate a single pixel response, even in the best case scenario of an idealized step edge. To workaround this issue, many existing algorithms employ some pre-processing steps of thinning or non-maximization suppression to reduce the edge to single pixel width responses before Hough transform is applied.